Heterogeneity of miRNA expression in localized prostate cancer with clinicopathological correlations

INTRODUCTION In the last decade microRNAs (miRNAs) have been widely investigated in prostate cancer (PCa) and have shown to be promising biomarkers in diagnostic, prognostic and predictive settings. However, tumor heterogeneity may influence miRNA expression. The aims of this study were to assess the impact of tumor heterogeneity, as demonstrated by a panel of selected miRNAs in PCa, and to correlate miRNA expression with risk profile and patient outcome. MATERIAL AND METHODS Prostatectomy specimens and matched, preoperative needle biopsies from a retrospective cohort of 49 patients, who underwent curatively intended surgery for localized PCa, were investigated with a panel of 6 miRNAs (miRNA-21, miRNA-34a, miRNA-125b, miRNA-126, miRNA-143, and miRNA-145) using tissue micro-array (TMA) and in situ hybridization (ISH). Inter- and intra-patient variation was assessed using intra-class correlation (ICC). RESULTS Four miRNAs (miRNA-21, miRNA-34a, miRNA-125, and miRNA-126) were significantly upregulated in PCa compared to benign prostatic hyperplasia (BPH), and except for miRNA-21 these miRNAs documented a positive correlation between the expression level in PCa cores and their matched BPH cores, (r > 0.72). The ICC varied from 0.451 to 0.764, with miRNA-34a showing an intra-tumoral heterogeneity accounting for less than 50% of the total variation. Regarding clinicopathological outcomes, only miRNA-143 showed potential as a prognostic marker with a higher expression correlating with longer relapse-free survival (p = 0.016). CONCLUSION The present study documents significant upregulation of the expression of miRNA-21, miRNA-34a, miRNA-125, and miRNA-126 in PCa compared to BPH and suggests a possible prognostic value associated with the expression of miRNA-143. The results, however, document intra-tumoral heterogeneity in the expression of various miRNAs calling for caution when using these tumor tissue biomarkers in prognostic and predictive settings.